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# SPDX-FileCopyrightText: Copyright 2023-2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Tests for module mlia.nn.rewrite.core.train."""
# pylint: disable=too-many-arguments
from __future__ import annotations
from contextlib import ExitStack as does_not_raise
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Any
import numpy as np
import pytest
import tensorflow as tf
from keras.api._v2 import keras # Temporary workaround for now: MLIA-1107
from mlia.nn.rewrite.core.train import augment_fn_twins
from mlia.nn.rewrite.core.train import AUGMENTATION_PRESETS
from mlia.nn.rewrite.core.train import LearningRateSchedule
from mlia.nn.rewrite.core.train import mixup
from mlia.nn.rewrite.core.train import train
from mlia.nn.rewrite.core.train import TrainingParameters
from tests.utils.rewrite import MockTrainingParameters
def replace_fully_connected_with_conv(
input_shape: Any, output_shape: Any
) -> keras.Model:
"""Get a replacement model for the fully connected layer."""
for name, shape in {
"Input": input_shape,
"Output": output_shape,
}.items():
if len(shape) != 1:
raise RuntimeError(f"{name}: shape (N,) expected, but it is {input_shape}.")
model = keras.Sequential(name="RewriteModel")
model.add(keras.Input(input_shape))
model.add(keras.layers.Reshape((1, 1, input_shape[0])))
model.add(keras.layers.Conv2D(filters=output_shape[0], kernel_size=(1, 1)))
model.add(keras.layers.Reshape(output_shape))
return model
def check_train(
tflite_model: Path,
tfrecord: Path,
train_params: TrainingParameters = MockTrainingParameters(),
use_unmodified_model: bool = False,
quantized: bool = False,
) -> None:
"""Test the train() function."""
with TemporaryDirectory() as tmp_dir:
output_file = Path(tmp_dir, "out.tflite")
result = train(
source_model=str(tflite_model),
unmodified_model=str(tflite_model) if use_unmodified_model else None,
output_model=str(output_file),
input_tfrec=str(tfrecord),
replace_fn=replace_fully_connected_with_conv,
input_tensors=["sequential/flatten/Reshape"],
output_tensors=["StatefulPartitionedCall:0"],
train_params=train_params,
)
assert len(result[0][0]) == 2
assert all(
res >= 0.0 for res in result[0][0]
), f"Results out of bound: {result}"
assert output_file.is_file()
if quantized:
interpreter = tf.lite.Interpreter(model_path=str(output_file))
interpreter.allocate_tensors()
# Check that the quantization parameters are non-zero
assert all(interpreter.get_output_details()[0]["quantization"])
assert all(interpreter.get_input_details()[0]["quantization"])
dtypes = []
for tensor_detail in interpreter.get_tensor_details():
dtypes.append(tensor_detail["dtype"])
assert all(np.issubdtype(dtype, np.integer) for dtype in dtypes)
@pytest.mark.parametrize(
(
"batch_size",
"show_progress",
"augmentation_preset",
"lr_schedule",
"use_unmodified_model",
"num_procs",
),
(
(1, False, AUGMENTATION_PRESETS["none"], "cosine", False, 2),
(32, True, AUGMENTATION_PRESETS["gaussian"], "late", True, 1),
(2, False, AUGMENTATION_PRESETS["mixup"], "constant", True, 0),
(
1,
False,
AUGMENTATION_PRESETS["mix_gaussian_large"],
"cosine",
False,
2,
),
),
)
def test_train_fp32(
test_tflite_model_fp32: Path,
test_tfrecord_fp32: Path,
batch_size: int,
show_progress: bool,
augmentation_preset: tuple[float | None, float | None],
lr_schedule: LearningRateSchedule,
use_unmodified_model: bool,
num_procs: int,
) -> None:
"""Test the train() function with valid parameters."""
check_train(
tflite_model=test_tflite_model_fp32,
tfrecord=test_tfrecord_fp32,
train_params=MockTrainingParameters(
batch_size=batch_size,
show_progress=show_progress,
augmentations=augmentation_preset,
learning_rate_schedule=lr_schedule,
num_procs=num_procs,
),
use_unmodified_model=use_unmodified_model,
)
@pytest.mark.parametrize(
(
"batch_size",
"show_progress",
"augmentation_preset",
"lr_schedule",
"use_unmodified_model",
"num_procs",
),
(
(1, False, AUGMENTATION_PRESETS["none"], "cosine", False, 2),
(32, True, AUGMENTATION_PRESETS["gaussian"], "late", True, 1),
(2, False, AUGMENTATION_PRESETS["mixup"], "constant", True, 0),
(
1,
False,
AUGMENTATION_PRESETS["mix_gaussian_large"],
"cosine",
False,
2,
),
),
)
def test_train_int8(
test_tflite_model: Path,
test_tfrecord: Path,
batch_size: int,
show_progress: bool,
augmentation_preset: tuple[float | None, float | None],
lr_schedule: LearningRateSchedule,
use_unmodified_model: bool,
num_procs: int,
) -> None:
"""Test the train() function with valid parameters."""
check_train(
tflite_model=test_tflite_model,
tfrecord=test_tfrecord,
train_params=MockTrainingParameters(
batch_size=batch_size,
show_progress=show_progress,
augmentations=augmentation_preset,
learning_rate_schedule=lr_schedule,
num_procs=num_procs,
),
use_unmodified_model=use_unmodified_model,
quantized=True,
)
def test_train_invalid_schedule(
test_tflite_model_fp32: Path,
test_tfrecord_fp32: Path,
) -> None:
"""Test the train() function with an invalid schedule."""
with pytest.raises(AssertionError):
check_train(
tflite_model=test_tflite_model_fp32,
tfrecord=test_tfrecord_fp32,
train_params=MockTrainingParameters(
learning_rate_schedule="unknown_schedule",
),
)
def test_train_invalid_augmentation(
test_tflite_model_fp32: Path,
test_tfrecord_fp32: Path,
) -> None:
"""Test the train() function with an invalid augmentation."""
with pytest.raises(AssertionError):
check_train(
tflite_model=test_tflite_model_fp32,
tfrecord=test_tfrecord_fp32,
train_params=MockTrainingParameters(
augmentations=(1.0, 2.0, 3.0),
),
)
def test_mixup() -> None:
"""Test the mixup() function."""
src = np.array((1, 2, 3))
dst = mixup(rng=np.random.default_rng(123), batch=src)
assert src.shape == dst.shape
assert np.all(dst >= 0.0)
assert np.all(dst <= 3.0)
@pytest.mark.parametrize(
"augmentations, expected_error",
[
(AUGMENTATION_PRESETS["none"], does_not_raise()),
(AUGMENTATION_PRESETS["mix_gaussian_large"], does_not_raise()),
((None,) * 3, pytest.raises(AssertionError)),
],
)
def test_augment_fn_twins(augmentations: tuple, expected_error: Any) -> None:
"""Test function augment_fn()."""
dataset = tf.data.Dataset.from_tensor_slices({"a": [1, 2, 3], "b": [4, 5, 6]})
with expected_error:
fn_twins = augment_fn_twins(dataset, augmentations) # type: ignore
assert len(fn_twins) == 2
def test_train_checkpoint(
test_tflite_model: Path,
test_tfrecord: Path,
) -> None:
"""Test the train() function with valid checkpoint parameters."""
check_train(
tflite_model=test_tflite_model,
tfrecord=test_tfrecord,
train_params=MockTrainingParameters(steps=64, checkpoint_at=[24, 32]),
use_unmodified_model=False,
quantized=True,
)
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